Abstract

Challenges on the Internet Infrastructure such as distributed Denial of Service Attacks are becoming more and more elaborate nowadays. DDoS has emerged as a growing threat not only to business and organizations but also to national security and public safety. Incidents such as the collapse of the Estonian network infrastructure in 2007 and the Wikileaks related DDoS attacks against so called anti free- speech organization, have suggested that DDoS is now being used as a new political weapon. DDoS attacks have become more dynamic and intelligent than before, prompting for equally advanced response in dealing with the attack. In this position paper, we aim to improve the D2R2+DR Resilience Strategy by adopting some learning and evolving components within it. We propose two schemes for our case studies. The first is focusing on classification and feature extraction, while the latter aims to capitalise on evolving intelligent techniques to control policy parameters. Both of these components will be experimented using a clean pipe (tunnelled) approach to emulate an attack scenario in a real ISP network. The novelty of our proposal lies in the application of a new, evolving intelligent technique which is computationally light, economical and sustainable, to our Resilience Strategy.